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mio-hiehei/css_methods_python: Self-explanatory introduction to Computational Social Science methods in Python (Work in progress) · GitHub

 
 

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Introduction to Computational Social Science methods with Python

This repository will grow to house a full introductory course consisting of self-explanatory teaching modules in the Jupyter Notebook format. The final course will consist of five sections with 14 sessions that will allow easy exploration of data with a minimum of coding skills, but gradually lead participants to acquire more coding skills in Python. These resources are provided as part of the Social ComQuant project.

Notebooks are developed for Anaconda 2022.10 which can be downloaded here.

Section D: Data analysis methods

Session 7: Network analysis

Session 8: Unsupervised machine learning

Session 9: Statistics & supervised machine learning

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Self-explanatory introduction to Computational Social Science methods in Python (Work in progress)

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